1. Field of the Invention
The present invention generally relates to a technology for discriminating a specific object in an image, and specifically relates to discriminating a specific object based on an image shot by a monocular camera.
2. Description of the Related Art
Technologies have been known that aim at avoiding a collision of a vehicle with an obstacle (for example, a pedestrian) in a driving path of the vehicle. Such technologies include capturing an image (for example, a photograph) of a region in front of the vehicle, and detecting and discriminating the obstacle from among various other objects in the image.
Japanese Patent Application Laid-open No. 2003-502745 discloses a technology that acquires a pair of stereo images of an area in front of the vehicle, detects objects in the stereo images, and discriminates a potential obstacle from among the detected objects by applying an object discriminating method using a neural network. The neural network is caused to learn various forms (or shapes) of pedestrians, whereby it becomes possible to accurately recognize a pedestrian in an image even if the pedestrian has a slightly different form.
Each of the stereo images is acquired with a separate camera attached to the vehicle. Thus, the technology disclosed in Japanese Patent Application Laid-open No. 2003-502745 requires two cameras, which makes the structure complicated and increases the manufacturing cost of the system.
On the other hand, to solve such drawbacks, a background difference method, which detects an object (for example, a pedestrian) based on a difference between a pixel value (such as brightness) of a current image and a pixel value of a previous image using a monocular camera, can be considered. However, if the object is stationary, the pixel value of the current image and the pixel value of the previous image are same, which makes detection of the object difficult.
Another approach could be to apply the neural network to an image shot by a monocular camera, i.e. an image shot by a single camera. However, if the neural network is applied over the whole area of the image it takes a lot of time to discriminate an object from the image thereby resulting in inefficient processing.